Taxonomy_correlation

library(here)

## here() starts at /home/natasa/Desktop/master_project/Master-Project-Natasa-Mortvanski

library(phyloseq)
library(qiime2R)
library(microbiome)

## Loading required package: ggplot2

## 
## microbiome R package (microbiome.github.com)
##     
## 
## 
##  Copyright (C) 2011-2021 Leo Lahti, 
##     Sudarshan Shetty et al. <microbiome.github.io>

## 
## Attaching package: 'microbiome'

## The following object is masked from 'package:ggplot2':
## 
##     alpha

## The following object is masked from 'package:base':
## 
##     transform

library(microbiomeutilities)

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## Warning: replacing previous import 'ggplot2::alpha' by 'microbiome::alpha' when
## loading 'microbiomeutilities'

library(tidyr)
library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

library(corrplot)

## corrplot 0.92 loaded

library(flextable)
library(writexl)

# Creating phyloseq object

physeq <- qza_to_phyloseq(
  features= "~/Desktop/master_project/Data_Hospital_donors/R2960/SCFA_QIIME2_analysis/table-dada2.qza",
  tree= "~/Desktop/master_project/Data_Hospital_donors/R2960/SCFA_QIIME2_analysis/rooted-tree.qza",
  taxonomy= "~/Desktop/master_project/Data_Hospital_donors/R2960/SCFA_QIIME2_analysis/taxonomy.qza",
  metadata = "~/Desktop/master_project/Master-Project-Natasa-Mortvanski/01_tidy_data/SCFA_alpha_donors.tsv"
  )

Taxonomic level: Family

Creating a table of abundances on Family level:

# tax_glom function merges species that have the same taxonomy at a certain 
# taxonomic rank - in this case family level
glom <- tax_glom(physeq, taxrank=rank_names(physeq)[5])

# psmelt function melts phyloseq data object into large data.frame 
smelted_data <- psmelt(glom) 

abundances_fam <- smelted_data %>% select(-c(Kingdom, Phylum, Class, Order)) %>%
        spread(Family, Abundance) %>%
  group_by(Sample) %>%
  summarize(across(3:63, ~unique(na.omit(.x))),.groups = 'drop')

abundances_fam

## # A tibble: 104 × 62
##    Sample donor_donation acetic_acid propionic_acid butyric_acid shannon_entropy
##    <chr>  <chr>                <dbl>          <dbl>        <dbl>           <dbl>
##  1 A1-Pl… CGR32F_7th           336.            77.3         75.8            6.81
##  2 A10-P… CLA33F_11th          177.            60.3        163.             6.63
##  3 A11-P… MBD34F_3rd            43.7           22.8         24.1            7.18
##  4 A12-P… FBG21M_4th           187.            46.0         42.6            6.69
##  5 A2-Pl… RTF49M_4th            52.3           14.2         16.1            6.86
##  6 A3-Pl… SMP48F_6th            47.0           18.6         17.6            6.65
##  7 A3-Pl… RTF49M_10th          369.           131.          87.1            6.42
##  8 A4-Pl… RTF49M_15th          131.            36.4         82.1            6.52
##  9 A5-Pl… EDR39F_1st           126.            67.1         57.8            6.36
## 10 A6-Pl… ATH27F_1st            67.0           32.3         25.3            6.82
## # ℹ 94 more rows
## # ℹ 56 more variables: chao1 <dbl>, fisher_alpha <dbl>, margalef <dbl>,
## #   gini_index <dbl>, menhinick <dbl>, strong <dbl>, simpson <dbl>,
## #   faith_pd <dbl>, pielou_evenness <dbl>, `[Barnesiellaceae]` <dbl>,
## #   `[Cerasicoccaceae]` <dbl>, `[Mogibacteriaceae]` <dbl>,
## #   `[Odoribacteraceae]` <dbl>, `[Paraprevotellaceae]` <dbl>,
## #   `[Tissierellaceae]` <dbl>, Alcaligenaceae <dbl>, …

write.table(abundances_fam, file = here("01_tidy_data", "abundance_family.tsv"), row.names=FALSE, sep="\t", quote=FALSE)

First lets check correlations between alpha diversity metrics and family abundances:

# Save results as data frame
tax_alpha_cor <- as.data.frame(cor(abundances_fam[, c(6:15)], abundances_fam[,c(16:50)]))
tax_alpha_cor %>% tibble::rownames_to_column() %>% flextable()

rowname	[Barnesiellaceae]	[Cerasicoccaceae]	[Mogibacteriaceae]	[Odoribacteraceae]	[Paraprevotellaceae]	[Tissierellaceae]	Alcaligenaceae	Anaeroplasmataceae	Bacteroidaceae	Bifidobacteriaceae	Campylobacteraceae	Carnobacteriaceae	Christensenellaceae	Clostridiaceae	Coriobacteriaceae	Dehalobacteriaceae	Desulfovibrionaceae	Elusimicrobiaceae	Enterobacteriaceae	Enterococcaceae	Erysipelotrichaceae	Eubacteriaceae	Fusobacteriaceae	Gemellaceae	Lachnospiraceae	Lactobacillaceae	Leptotrichiaceae	Leuconostocaceae	Methanobacteriaceae	mitochondria	Moraxellaceae	Oxalobacteraceae	Pasteurellaceae	Peptococcaceae	Peptostreptococcaceae
shannon_entropy	0.1961739	0.04587657	0.42021153	0.35326132	-0.10191302	0.15721016	0.012886096	0.01560096	-0.10401313	0.11404702	-0.13617509	0.08882543	0.38080772	0.047159992	0.21751498	0.16634117	0.48943128	0.0001774658	-0.2906366	0.11363339	0.00382816	0.04727198	0.09246265	0.05074282	0.110241368	0.05627490	0.01477416	0.036780567	0.42413481	-0.108218796	0.036780567	0.3966001	0.035661043	0.22682347	-0.04046494583
chao1	0.3983393	0.06216873	0.44673458	0.57132712	-0.11927104	0.09062786	-0.015945179	-0.08900386	0.06078133	0.14902624	-0.05413649	0.06547280	0.44115957	0.047179832	0.25969848	0.21082246	0.54066280	0.0755030083	-0.3274936	0.12923304	-0.13100630	-0.01609057	0.03090747	0.01174541	0.010581630	0.14336458	-0.02351381	0.042111939	0.38925318	0.014068695	0.042111939	0.5081410	-0.003174261	0.29422608	-0.00224146078
fisher_alpha	0.3936996	0.07091003	0.44612542	0.57158136	-0.13397330	0.09686233	-0.019397709	-0.09423500	0.05442319	0.14663316	-0.05118035	0.06909077	0.45375083	0.046097431	0.26354678	0.21765952	0.55212263	0.0624132416	-0.3438112	0.13301278	-0.13194528	-0.01817683	0.03711979	0.01023980	0.009574233	0.14914291	-0.02197679	0.028522431	0.40319678	-0.005772743	0.028522431	0.5168397	-0.009185043	0.30244158	-0.00001874811
margalef	0.3973124	0.06998401	0.44809716	0.57010029	-0.13619450	0.09749610	-0.015737951	-0.09103355	0.05468808	0.15119899	-0.04968787	0.06813527	0.45133338	0.050852669	0.26443582	0.21862052	0.55128947	0.0643366351	-0.3459213	0.13466851	-0.13082091	-0.01606918	0.03958950	0.01256500	0.012095435	0.15189829	-0.01941388	0.030786743	0.39658681	-0.005341178	0.030786743	0.5137065	-0.010093940	0.29894245	-0.00058696851
gini_index	-0.2744844	-0.07061008	-0.43738143	-0.47112177	0.12565482	-0.15186035	-0.001674808	0.01691102	0.03200879	-0.11179463	0.09933723	-0.08644533	-0.40048430	-0.048434934	-0.23175553	-0.16334157	-0.50953966	0.0151382790	0.3085910	-0.10099551	0.02995650	-0.03869648	-0.06774699	-0.04648364	-0.109638267	-0.06992236	0.00722504	-0.026077484	-0.46017354	0.097291881	-0.026077484	-0.4275486	-0.021119345	-0.26707969	0.01824285052
menhinick	0.3973124	0.06998401	0.44809716	0.57010029	-0.13619450	0.09749610	-0.015737951	-0.09103355	0.05468808	0.15119899	-0.04968787	0.06813527	0.45133338	0.050852669	0.26443582	0.21862052	0.55128947	0.0643366351	-0.3459213	0.13466851	-0.13082091	-0.01606918	0.03958950	0.01256500	0.012095435	0.15189829	-0.01941388	0.030786743	0.39658681	-0.005341178	0.030786743	0.5137065	-0.010093940	0.29894245	-0.00058696851
strong	0.2838715	0.06257516	0.15721202	0.26712286	-0.01478512	-0.05060710	-0.049059400	-0.13562485	0.11517395	0.11523776	0.10206079	0.01395815	0.23867993	-0.017552577	0.14899240	0.19784778	0.24840376	0.2308996280	-0.1147883	0.11663887	-0.18732150	-0.14083349	-0.01563013	-0.08508918	-0.292377731	0.17218141	-0.01629158	0.019243860	-0.01445655	0.193130418	0.019243860	0.2829366	-0.053582330	0.16556683	0.03796192806
simpson	-0.1482763	-0.02576009	0.22892981	-0.09787139	0.02939851	0.14114608	0.018318426	0.10745850	-0.29376183	0.02676426	-0.24701477	0.08533862	0.21786878	-0.012874742	0.09241626	0.08488361	0.27291167	-0.0152780533	-0.1015751	0.07548602	0.12344614	0.06359883	0.11583283	0.04800740	0.103548997	-0.05713842	0.04775054	0.035672617	0.29645593	-0.125998887	0.035672617	0.1856331	0.094908668	0.05686916	-0.09389218815
faith_pd	0.2845479	0.11051965	0.40234176	0.37278714	0.02526113	0.11640291	-0.137494964	-0.12712645	-0.03442883	0.20266925	-0.02828822	0.04559428	0.44693338	0.008489347	0.35554525	0.15031176	0.47533031	0.1585353280	-0.1709059	0.15630988	-0.04031295	-0.03580521	0.03968340	-0.03134623	-0.234888894	0.08073983	-0.06119944	-0.054986273	0.35482468	0.528622964	-0.054986273	0.3597795	-0.136334961	0.31505715	-0.04653492509
pielou_evenness	-0.3778888	-0.04163143	-0.03466668	-0.36034803	0.06859167	0.11427413	0.024317169	0.16931815	-0.30487484	-0.09177427	-0.18934919	0.04902762	-0.07733878	-0.041278643	-0.07766181	-0.08720601	-0.07324653	-0.1315143919	0.0867717	-0.03802944	0.23102940	0.10166737	0.08480880	0.05452080	0.166035449	-0.18446494	0.04095021	-0.003949148	0.13015699	-0.200237211	-0.003949148	-0.1521718	0.085048126	-0.09477153	-0.07529454866

# Plot correlation 
matrix_tax_alpha <- cor(abundances_fam[, c(6:15)], abundances_fam[,c(16:50)] , method = "spearman")
corrplot(matrix_tax_alpha, method="circle", tl.col = "black", tl.srt = 90, col.lim = c(-1,1), insig = "blank")

Then lets check correlations between SCFA and family abundances:

library(dichromat)
library(RColorBrewer)
library(graphics)


# Save results as data frame
tax_SCFA_cor <- as.data.frame(cor(abundances_fam[, c(3:5)], abundances_fam[,c(16:50)]))
tax_SCFA_cor %>% tibble::rownames_to_column() %>% flextable()

rowname	[Barnesiellaceae]	[Cerasicoccaceae]	[Mogibacteriaceae]	[Odoribacteraceae]	[Paraprevotellaceae]	[Tissierellaceae]	Alcaligenaceae	Anaeroplasmataceae	Bacteroidaceae	Bifidobacteriaceae	Campylobacteraceae	Carnobacteriaceae	Christensenellaceae	Clostridiaceae	Coriobacteriaceae	Dehalobacteriaceae	Desulfovibrionaceae	Elusimicrobiaceae	Enterobacteriaceae	Enterococcaceae	Erysipelotrichaceae	Eubacteriaceae	Fusobacteriaceae	Gemellaceae	Lachnospiraceae	Lactobacillaceae	Leptotrichiaceae	Leuconostocaceae	Methanobacteriaceae	mitochondria	Moraxellaceae	Oxalobacteraceae	Pasteurellaceae	Peptococcaceae	Peptostreptococcaceae
acetic_acid	0.20524171	-0.09267731	-0.06139248	0.17632447	0.0408958294	-0.12083575	0.04613958	0.00224551	0.11191166	-0.01613749	-0.02146923	0.03069292	-0.1651295	0.095361137	-0.08406569	-0.1566521	-0.09321140013	0.20371895	0.1212529	-0.09577773	-0.008895018	-0.05338218	-0.1516329	0.04195436	0.1878028	0.02743697	-0.06169235	0.009990479	-0.11222351	0.03916014	0.009990479	-0.2014101	0.2155984	-0.11479548	-0.02437538
propionic_acid	0.05204696	-0.11126960	0.02929166	-0.02678931	0.0952342132	-0.09459811	-0.03003317	-0.03991217	-0.10476762	-0.04559440	0.01004809	0.03538336	-0.1134535	0.029136059	-0.07622693	-0.1017626	-0.00001110557	0.22930148	0.2051389	-0.08114750	0.053369255	-0.06856381	-0.1345117	0.01518367	0.0229006	0.09200223	-0.07703053	-0.010252414	-0.09729968	0.06968642	-0.010252414	-0.1431086	0.2591636	-0.08610552	-0.09916577
butyric_acid	-0.13604957	-0.10526382	-0.16101055	-0.17232419	-0.0004175809	-0.09642060	-0.01072453	-0.04490758	-0.07197388	-0.04747222	-0.02421638	0.16074285	-0.2196374	0.008502025	-0.12456361	-0.1438063	-0.15658623990	0.05946432	0.2165128	-0.10942620	0.112840386	-0.08831867	-0.1247286	0.06561711	0.1654345	-0.03863936	-0.02175125	0.018979201	-0.16384997	-0.04482030	0.018979201	-0.2561053	0.3480001	-0.16527575	-0.07693781

scalebluered <- colorRampPalette(brewer.pal(8, "RdBu"))(50)

# Plot correlation 
matrix_tax_SCFA <- cor(abundances_fam[, c(3:5)], abundances_fam[,c(16:50)] , method = "spearman")

#corrplot(matrix_tax_SCFA, method="circle", tl.col = "black", tl.srt = 90, col.lim = c(-1,1), is.corr = FALSE) 

# I did not like how the generic legend looked like, so I decided to generate legend separately. However,
# I did not know how to put it on right side of the plot to look like the other plots, so I put it on the left side...

corrplot(matrix_tax_SCFA, method="circle", tl.col = "black", tl.srt = 90, cl.pos="n", insig = "blank") 
colorlegend(xlim=c(-3,0), ylim=c(10,5), scalebluered, c(seq(-1,1,1)), align="r", vertical=TRUE, addlabels=TRUE)

Finally, lets check correlations between alpha diversity metrics and SCFA:

# Save results as data frame
alpha_SCFA_cor <- as.data.frame(cor(abundances_fam[, c(3:5)], abundances_fam[,c(6:15)]))
alpha_SCFA_cor %>% tibble::rownames_to_column() %>% flextable()

rowname	shannon_entropy	chao1	fisher_alpha	margalef	gini_index	menhinick	strong	simpson	faith_pd	pielou_evenness
acetic_acid	-0.2646461	-0.1095884	-0.1199726	-0.1176453	0.2153806	-0.1176453	0.13095051	-0.3855660	-0.01865583	-0.303903050
propionic_acid	-0.2595886	-0.1444708	-0.1557334	-0.1534482	0.2497485	-0.1534482	0.17265440	-0.2722972	-0.01298810	-0.229056041
butyric_acid	-0.3157031	-0.3299432	-0.3334144	-0.3327643	0.3318925	-0.3327643	-0.08957821	-0.1894455	-0.27550113	0.004129023

# Plot correlation 
matrix_alpha_SCFA <- cor(abundances_fam[, c(3:5)], abundances_fam[,c(6:15)] , method = "spearman")
corrplot(matrix_alpha_SCFA, method="circle", tl.col = "black", tl.srt = 90, col.lim = c(-1,1), insig = "blank")

Correlation plot for family abundancies vs. SCFA and alpha diversity all together:

matrix_all <- cor(abundances_fam[, c(3:15)], abundances_fam[,c(16:50)] , method = "spearman")
corrplot(matrix_all, method="circle", tl.col = "black", tl.srt = 90, col.lim = c(-1,1), insig = "blank")

Taxonomic level: Genus

Creating a table of abundances on Genus level:

# tax_glom function merges species that have the same taxonomy at a certain 
# taxonomic rank - in this case genus level
glom <- tax_glom(physeq, taxrank=rank_names(physeq)[6])

# psmelt function melts phyloseq data object into large data.frame 
smelted_data <- psmelt(glom) 

# Problem with Clostridium!
abundances_gen <- smelted_data %>% select(-c(Kingdom, Phylum, Class, Order, Family)) %>%
        spread(Genus, Abundance) %>%
  group_by(Sample) %>% select(-c(Clostridium)) %>%
  summarize(across(2:94, ~unique(na.omit(.x))),.groups = 'drop')

abundances_gen

## # A tibble: 104 × 94
##    Sample            peti donor_donation acetic_acid propionic_acid butyric_acid
##    <chr>            <int> <chr>                <dbl>          <dbl>        <dbl>
##  1 A1-Placa-E2  216133466 CGR32F_7th           336.            77.3         75.8
##  2 A10-Placa-E2 226170254 CLA33F_11th          177.            60.3        163. 
##  3 A11-Placa-E2 226175993 MBD34F_3rd            43.7           22.8         24.1
##  4 A12-Placa-E2 226180072 FBG21M_4th           187.            46.0         42.6
##  5 A2-Placa-E2  216133396 RTF49M_4th            52.3           14.2         16.1
##  6 A3-Placa-E1  226185617 SMP48F_6th            47.0           18.6         17.6
##  7 A3-Placa-E2  216136699 RTF49M_10th          369.           131.          87.1
##  8 A4-Placa-E2  216139548 RTF49M_15th          131.            36.4         82.1
##  9 A5-Placa-E2  216142212 EDR39F_1st           126.            67.1         57.8
## 10 A6-Placa-E2  216144512 ATH27F_1st            67.0           32.3         25.3
## # ℹ 94 more rows
## # ℹ 88 more variables: shannon_entropy <dbl>, chao1 <dbl>, fisher_alpha <dbl>,
## #   margalef <dbl>, gini_index <dbl>, menhinick <dbl>, strong <dbl>,
## #   simpson <dbl>, faith_pd <dbl>, pielou_evenness <dbl>,
## #   `[Eubacterium]` <dbl>, `[Ruminococcus]` <dbl>, `1-68` <dbl>,
## #   Acidaminococcus <dbl>, Acinetobacter <dbl>, Adlercreutzia <dbl>,
## #   AF12 <dbl>, Akkermansia <dbl>, Alistipes <dbl>, Anaerostipes <dbl>, …

write.table(abundances_gen, file = here("01_tidy_data", "abundance_genera.tsv"), row.names=FALSE, sep="\t", quote=FALSE)

First lets check correlations between alpha diversity metrics and family abundances:

# Save results as data frame
tax_alpha_cor <- as.data.frame(cor(abundances_gen[, c(7:16)], abundances_gen[,c(17:94)]))
tax_alpha_cor %>% tibble::rownames_to_column() %>% flextable()

rowname	[Eubacterium]	[Ruminococcus]	1-68	Acidaminococcus	Acinetobacter	Adlercreutzia	AF12	Akkermansia	Alistipes	Anaerostipes	Anaerotruncus	Bacteroides	Barnesiella	Bifidobacterium	Bilophila	Blautia	Butyricicoccus	Butyricimonas	Campylobacter	Catenibacterium	cc_115	Citrobacter	Collinsella	Coprobacillus	Coprococcus	Dehalobacterium	Desulfovibrio	Dialister	Dorea	Enterobacter	Enterococcus	Epulopiscium	Faecalibacterium	Fusobacterium	Gemmiger	Gluconacetobacter	Granulicatella	Haemophilus	Hafnia	Holdemania	human	Klebsiella	Lachnobacterium	Lachnospira	Lactobacillus	Lactococcus	Megamonas	Megasphaera	Methanobrevibacter	Mitsuokella	Morganella	Odoribacter	Oscillospira	Oxalobacter	Parabacteroides	Paraprevotella	Peptococcus	Phascolarctobacterium	Porphyromonas	Prevotella	Pseudomonas	Pseudoramibacter_Eubacterium	rc4-4	RFN20	Rikenella	Roseburia	Ruminococcus	Serratia	Slackia	SMB53	Sneathia	Streptococcus	Sutterella	Synergistes	Turicibacter	Veillonella	WAL_1855D	Weissella
shannon_entropy	0.07159998	0.13938767	0.23103013	0.108286334	0.036780567	0.068457389	0.17354547	0.21747245	0.48221400	-0.01550384	0.23152449	-0.10402188	0.04431397	0.11404702	0.27230601	-0.07156724	0.06095575072	0.452660506	-0.13617509	-0.270733340	0.30015993	0.02763261	0.14846651	0.01307427	0.27728119	0.16634117	0.39709750	-0.075214784	0.11158385	0.06751459	0.11363339	0.057805254	-0.23290899	0.09246265	-0.05628918	0.03779447	0.08882543	0.035661043	0.09788973	-0.09241026	0.309043913	0.06408121	0.32438746	0.0002913416	0.05627490	-0.15010474	0.18472891	0.15326186	0.42413481	0.17225303	-0.03861396	0.18666254	0.4686748	0.3966001	0.18359537	-0.10191302	0.18119343	0.033820084	0.08170784	0.2165781	0.015380381	0.04727198	0.25905638	0.08918314	-0.14074391	-0.06853381	0.26377240	0.03423626	0.229274107	-0.08552451	0.01477416	-0.007667943	0.012886096	0.19638858	-0.052283432	-0.2861082	0.13175347	0.036780567
chao1	-0.03638702	0.10205368	0.21274291	0.152584970	0.042111939	-0.021874857	0.31611978	0.36348536	0.52420789	-0.16313867	0.13168898	0.06077046	0.06291673	0.14902624	0.30325114	-0.05674697	0.07743809648	0.698884646	-0.05413649	-0.272253842	0.33423134	-0.06743896	0.18521457	0.09730856	0.39460160	0.21082246	0.41982657	-0.088170642	0.04342752	0.01427595	0.12923304	0.091035791	-0.35887258	0.03090747	-0.02826961	-0.04645710	0.06547280	-0.003174261	0.03220064	-0.16174144	0.263675376	0.11035859	0.26796493	-0.0789801950	0.14336458	-0.09338715	0.14848375	0.18019220	0.38925318	0.20769049	-0.11731234	0.32352868	0.5775615	0.5081410	0.22001099	-0.11927104	0.15345588	0.018964675	0.02210724	0.3270622	-0.078345476	-0.01609057	0.32736930	0.04514859	-0.05404873	-0.13528621	0.34206429	-0.01356003	0.238517312	-0.02537611	-0.02351381	-0.022595562	-0.015945179	0.26277538	-0.053687155	-0.2089988	0.06692396	0.042111939
fisher_alpha	-0.04266201	0.10243623	0.22777165	0.142881861	0.028522431	-0.018170692	0.30695543	0.36372284	0.52843491	-0.17079839	0.13419861	0.05441420	0.04653088	0.14663316	0.30520335	-0.06533926	0.06058732764	0.705909829	-0.05118035	-0.284136402	0.34014791	-0.06547499	0.18610876	0.10918235	0.39736206	0.21765952	0.43297201	-0.104391958	0.04544028	0.01023980	0.13301278	0.073548985	-0.35961444	0.03711979	-0.03201998	-0.04366895	0.06909077	-0.009185043	0.03640441	-0.15871957	0.276935541	0.09784401	0.27146610	-0.0826432096	0.14914291	-0.09628639	0.15289732	0.18495988	0.40319678	0.20402245	-0.11322640	0.31929618	0.5838954	0.5168397	0.21710880	-0.13397330	0.15784148	0.027229629	0.02004396	0.3207720	-0.074451843	-0.01817683	0.32360956	0.03903789	-0.05125168	-0.13404434	0.34823021	-0.01303907	0.248360533	-0.02453051	-0.02197679	-0.029756605	-0.019397709	0.26766104	-0.070188588	-0.2262530	0.07148311	0.028522431
margalef	-0.04294028	0.10703291	0.22081353	0.147170247	0.030786743	-0.014988000	0.30635207	0.36329867	0.52660033	-0.16756017	0.13279219	0.05467900	0.04908215	0.15119899	0.31168381	-0.06455511	0.06160509981	0.703323138	-0.04968787	-0.285537297	0.33033472	-0.06380222	0.18849984	0.10661453	0.39602264	0.21862052	0.42764732	-0.098549087	0.04850559	0.01256500	0.13466851	0.075039558	-0.35843293	0.03958950	-0.02837994	-0.04210025	0.06813527	-0.010093940	0.03859606	-0.15337586	0.271504546	0.10022254	0.26794237	-0.0844359241	0.15189829	-0.09583334	0.15123247	0.18654035	0.39658681	0.20843923	-0.11498723	0.31896261	0.5851950	0.5137065	0.21497119	-0.13619450	0.15573587	0.026531909	0.02187530	0.3276695	-0.073015363	-0.01606918	0.31517779	0.04119917	-0.04990957	-0.12627784	0.34971356	-0.01086296	0.246135995	-0.02197471	-0.01941388	-0.026716509	-0.015737951	0.25725703	-0.067240540	-0.2252004	0.07290958	0.030786743
gini_index	-0.04878789	-0.14172151	-0.27101405	-0.124796782	-0.026077484	-0.039736261	-0.24861298	-0.25221615	-0.51722967	0.06726804	-0.23208294	0.03201768	-0.03675671	-0.11179463	-0.28727367	0.06540978	-0.08547496357	-0.554753489	0.09933723	0.286216292	-0.32914016	-0.01431225	-0.15083960	-0.05752248	-0.36608418	-0.16334157	-0.40605757	0.120295470	-0.10668560	-0.05076923	-0.10099551	-0.049507254	0.26796761	-0.06774699	0.06275676	-0.02455684	-0.08644533	-0.021119345	-0.08923833	0.10308008	-0.324204596	-0.04836574	-0.33604171	-0.0005443647	-0.06992236	0.14100810	-0.19708097	-0.12450000	-0.46017354	-0.14386411	0.05044059	-0.28083386	-0.5142416	-0.4275486	-0.23115889	0.12565482	-0.19431764	-0.024596955	-0.06700058	-0.1814911	0.002865844	-0.03869648	-0.28919881	-0.08152186	0.10261073	0.09975348	-0.33282357	-0.03122341	-0.239554579	0.08815003	0.00722504	0.012485752	-0.001674808	-0.26427198	0.063402678	0.2891960	-0.12184022	-0.026077484
menhinick	-0.04294028	0.10703291	0.22081353	0.147170247	0.030786743	-0.014988000	0.30635207	0.36329867	0.52660033	-0.16756017	0.13279219	0.05467900	0.04908215	0.15119899	0.31168381	-0.06455511	0.06160509981	0.703323138	-0.04968787	-0.285537297	0.33033472	-0.06380222	0.18849984	0.10661453	0.39602264	0.21862052	0.42764732	-0.098549087	0.04850559	0.01256500	0.13466851	0.075039558	-0.35843293	0.03958950	-0.02837994	-0.04210025	0.06813527	-0.010093940	0.03859606	-0.15337586	0.271504546	0.10022254	0.26794237	-0.0844359241	0.15189829	-0.09583334	0.15123247	0.18654035	0.39658681	0.20843923	-0.11498723	0.31896261	0.5851950	0.5137065	0.21497119	-0.13619450	0.15573587	0.026531909	0.02187530	0.3276695	-0.073015363	-0.01606918	0.31517779	0.04119917	-0.04990957	-0.12627784	0.34971356	-0.01086296	0.246135995	-0.02197471	-0.01941388	-0.026716509	-0.015737951	0.25725703	-0.067240540	-0.2252004	0.07290958	0.030786743
strong	-0.11188448	-0.08337943	-0.04996814	0.071174960	0.019243860	-0.096462430	0.11713086	0.30594809	0.12292211	-0.25971058	-0.21268317	0.11517161	0.04135788	0.11523776	0.11241541	-0.03837496	-0.04857870484	0.407033975	0.10206079	-0.001743082	0.06678907	-0.20773467	0.14316523	0.13947974	0.06207966	0.19784778	0.20260374	0.067816446	-0.15198084	-0.06290857	0.11663887	0.066683353	-0.31387998	-0.01563013	0.08471928	-0.15735407	0.01395815	-0.053582330	-0.09304711	-0.18367770	-0.004601054	0.16825930	-0.05350426	-0.2683619423	0.17218141	0.10346468	-0.04239236	0.20109981	-0.01445655	0.20442597	-0.20031183	0.09894205	0.2817832	0.2829366	-0.04764457	-0.01478512	-0.02263729	0.031020173	-0.04906963	0.4168351	-0.173196574	-0.14083349	0.09150597	-0.08175925	0.10619220	-0.11240150	0.08556134	-0.10744658	0.092844612	0.15272963	-0.01629158	-0.001553990	-0.049059400	-0.01111107	-0.009895018	0.1337865	-0.04518166	0.019243860
simpson	0.17206462	0.06577147	0.15170291	0.005033255	0.035672617	0.115782331	-0.08907821	0.01158831	0.26524339	0.14128105	0.22033065	-0.29376672	0.02580022	0.02676426	0.08618909	-0.06359497	-0.00004246016	-0.002365089	-0.24701477	-0.118277906	0.20002527	0.06446741	0.05925141	-0.12882887	-0.03998642	0.08488361	0.28448218	0.008442535	0.07707192	0.08907522	0.07548602	0.036444531	-0.05762422	0.11583283	-0.07036063	0.06613545	0.08533862	0.094908668	0.10397670	-0.07280287	0.258577001	0.04081203	0.26724234	0.0528378731	-0.05713842	-0.15884818	0.13461960	0.12737346	0.29645593	0.11718767	0.04140618	-0.13191872	0.1618745	0.1856331	0.03938481	0.02939851	0.13051522	0.050686613	0.09772102	0.1067618	0.063874585	0.06359883	0.15685844	0.09001612	-0.25339392	-0.01804240	-0.02631759	0.03298194	0.141150275	-0.10125470	0.04775054	-0.009406021	0.018318426	0.07519729	-0.020617179	-0.2214215	0.12461393	0.035672617
faith_pd	0.11948764	0.03200218	0.13900974	0.287049979	-0.054986273	-0.005281073	0.32182268	0.37658481	0.23631863	-0.33942636	0.06903994	-0.03442819	-0.02319745	0.20266925	0.22381638	-0.03717315	0.11658230895	0.542337613	-0.02828822	-0.091551030	0.23064248	-0.12478391	0.27069566	0.06733520	0.27548528	0.15031176	0.41544342	-0.092222016	0.04536866	-0.03616911	0.15630988	0.003829546	-0.45481567	0.03968340	-0.02106906	-0.09688830	0.04559428	-0.136334961	0.01215433	-0.27508335	0.305044126	0.17997298	0.15965525	-0.2860212562	0.08073983	-0.08821139	0.14449164	0.13522310	0.35482468	0.03901185	-0.08556328	0.15483481	0.2881591	0.3597795	0.07753525	0.02526113	0.14154497	-0.004425807	0.06395975	0.1933990	-0.128385848	-0.03580521	0.19379312	0.05027771	-0.03145027	-0.25646404	0.24383624	0.02773202	0.224224377	-0.01229593	-0.06119944	-0.022356535	-0.137494964	0.12863566	-0.116453816	-0.1157593	0.10119138	-0.054986273
pielou_evenness	0.21042975	0.03011787	0.08322478	-0.095873353	-0.003949148	0.132002879	-0.21863064	-0.24030542	-0.02679162	0.24755163	0.20908765	-0.30487445	-0.02237424	-0.09177427	-0.10673545	-0.02812479	-0.01307993607	-0.396300680	-0.18934919	0.010096994	0.03938567	0.15696295	-0.08077847	-0.15894402	-0.18496708	-0.08720601	0.01403213	-0.013154554	0.10293579	0.08648484	-0.03802944	-0.035991315	0.19715646	0.08480880	-0.09139849	0.13624348	0.04902762	0.085048126	0.09819751	0.05689466	0.129180976	-0.07826021	0.15076665	0.1700677747	-0.18446494	-0.12177272	0.08683748	-0.05591838	0.13015699	-0.08073323	0.15665210	-0.23306289	-0.1891298	-0.1521718	-0.03081689	0.06859167	0.07559217	0.016824346	0.09874500	-0.2303185	0.150463629	0.10166737	-0.01771059	0.07734198	-0.19641210	0.04219254	-0.15136302	0.06647489	0.003835268	-0.14854419	0.04095021	0.002504481	0.024317169	-0.02002618	-0.008113092	-0.1549767	0.10538298	-0.003949148

# Plot correlation 
matrix_tax_alpha_1 <- cor(abundances_gen[, c(7:16)], abundances_gen[,c(17:55)] , method = "spearman")
corrplot(matrix_tax_alpha_1, method="circle", tl.col = "black", tl.srt = 90, tl.cex =0.9, col.lim = c(-1,1), insig = "blank")

matrix_tax_alpha_2 <- cor(abundances_gen[, c(7:16)], abundances_gen[,c(56:94)] , method = "spearman")
corrplot(matrix_tax_alpha_2, method="circle", tl.col = "black", tl.srt = 90, tl.cex =0.9, col.lim = c(-1,1), insig = "blank")

Then lets check correlations between SCFA and family abundances:

# Save results as data frame
tax_SCFA_cor <- as.data.frame(cor(abundances_gen[, c(4:6)], abundances_gen[,c(17:94)]))
tax_SCFA_cor %>% tibble::rownames_to_column() %>% flextable()

rowname	[Eubacterium]	[Ruminococcus]	1-68	Acidaminococcus	Acinetobacter	Adlercreutzia	AF12	Akkermansia	Alistipes	Anaerostipes	Anaerotruncus	Bacteroides	Barnesiella	Bifidobacterium	Bilophila	Blautia	Butyricicoccus	Butyricimonas	Campylobacter	Catenibacterium	cc_115	Citrobacter	Collinsella	Coprobacillus	Coprococcus	Dehalobacterium	Desulfovibrio	Dialister	Dorea	Enterobacter	Enterococcus	Epulopiscium	Faecalibacterium	Fusobacterium	Gemmiger	Gluconacetobacter	Granulicatella	Haemophilus	Hafnia	Holdemania	human	Klebsiella	Lachnobacterium	Lachnospira	Lactobacillus	Lactococcus	Megamonas	Megasphaera	Methanobrevibacter	Mitsuokella	Morganella	Odoribacter	Oscillospira	Oxalobacter	Parabacteroides	Paraprevotella	Peptococcus	Phascolarctobacterium	Porphyromonas	Prevotella	Pseudomonas	Pseudoramibacter_Eubacterium	rc4-4	RFN20	Rikenella	Roseburia	Ruminococcus	Serratia	Slackia	SMB53	Sneathia	Streptococcus	Sutterella	Synergistes	Turicibacter	Veillonella	WAL_1855D	Weissella
acetic_acid	-0.048231715	0.1544897	-0.1102867	0.13862046	0.009990479	-0.02676645	0.16799779	0.13478994	-0.2750128	-0.07243646	-0.1364766	0.11191741	-0.008353112	-0.01613749	0.01269479	0.15646609	0.14005715	-0.01370241	-0.02146923	0.1388464	-0.1324437	0.0437654	-0.06968238	-0.06124696	0.1291944	-0.1566521	-0.10066299	0.2395926	0.01444078	0.08974546	-0.09577773	-0.02618438	0.15406588	-0.1516329	0.1813651	0.06470166	0.03069292	0.2155984	-0.04863183	-0.02589902	-0.2252298	0.18563814	-0.1757955	0.11992883	0.02743697	0.2371008	-0.11117575	-0.122929703	-0.11222351	-0.11731467	-0.06654224	0.24937286	-0.08860194	-0.2014101	0.001515321	0.0408958294	-0.10698489	-0.10590844	-0.09910456	0.03855663	0.05254562	-0.05338218	-0.09030307	-0.09721057	-0.01654294	0.2908791	0.166630062	-0.03145050	-0.002093324	0.1543014	-0.06169235	0.2088887	0.04613958	-0.02808312	0.02228463	0.1432133	-0.10890530	0.009990479
propionic_acid	-0.005735281	0.1266747	-0.1001720	-0.02788658	-0.010252414	-0.04976517	0.01861533	0.09966111	-0.1973662	-0.02520465	-0.1224400	-0.10476337	-0.006627257	-0.04559440	0.04907994	0.06281714	0.09146129	-0.12609920	0.01004809	0.2112414	-0.1467473	0.0460600	-0.05305150	-0.02237027	-0.1147851	-0.1017626	-0.01367881	0.3995076	0.01840001	0.10654161	-0.08114750	-0.00475195	0.08658573	-0.1345117	0.1680536	0.09640637	0.03538336	0.2591636	-0.03841114	-0.10965634	-0.1985604	0.20207626	-0.1937272	-0.01866294	0.09200223	0.2969093	-0.09717652	0.001720114	-0.09729968	0.01532995	-0.06424677	0.04566383	-0.08370994	-0.1431086	-0.139153838	0.0952342132	-0.09406458	-0.12919530	-0.06966460	0.23646185	0.07792443	-0.06856381	-0.13788048	-0.08440119	0.01392033	0.2594391	-0.008833628	-0.02459244	-0.008608915	0.1236279	-0.07703053	0.2725172	-0.03003317	-0.08722848	0.03776054	0.2193534	-0.08368842	-0.010252414
butyric_acid	-0.058493438	0.1239715	-0.1055359	-0.08559811	0.018979201	-0.02814483	-0.07869090	-0.03782660	-0.2344429	0.11569087	-0.1551789	-0.07196801	-0.096788329	-0.04747222	-0.05143746	0.04416191	0.10036190	-0.28548307	-0.02421638	0.1621426	-0.1473870	0.1204202	-0.09024779	-0.05457181	-0.1499684	-0.1438063	-0.13759479	0.3160254	0.04932059	0.04830233	-0.10942620	-0.05041052	0.34579200	-0.1247286	0.2825893	0.16983778	0.16074285	0.3480001	-0.06554484	0.01971079	-0.2149490	0.04000696	-0.1533753	0.11134722	-0.03863936	0.4003416	-0.10839353	-0.062927225	-0.16384997	-0.04191171	-0.06476452	-0.04890160	-0.22153577	-0.2561053	-0.104676715	-0.0004175809	-0.10395799	-0.06268824	-0.07022972	0.04870113	0.16002676	-0.08831867	-0.13235299	-0.09046454	-0.02115347	0.3575807	-0.003415168	-0.03250106	-0.074563052	0.1551168	-0.02175125	0.2833861	-0.01072453	-0.07077105	0.11348622	0.2325765	-0.08491102	0.018979201

# Plot correlation 
matrix_tax_SCFA_1 <- cor(abundances_gen[, c(4:6)], abundances_gen[,c(17:55)] , method = "spearman")
corrplot(matrix_tax_SCFA_1, method="circle", tl.col = "black", tl.srt = 90, cl.pos="n", insig = "blank")
colorlegend(xlim=c(-3,0), ylim=c(10,5), scalebluered, c(seq(-1,1,1)), align="r", vertical=TRUE, addlabels=TRUE)

matrix_tax_SCFA_2 <- cor(abundances_gen[, c(4:6)], abundances_gen[,c(56:94)] , method = "spearman")
corrplot(matrix_tax_SCFA_2, method="circle", tl.col = "black", tl.srt = 90, cl.pos="n", insig = "blank")
colorlegend(xlim=c(-3,0), ylim=c(10,5), scalebluered, c(seq(-1,1,1)), align="r", vertical=TRUE, addlabels=TRUE)

• Column “human” (from taxonomy file): k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__human; s__gut metagenome

Correlation plot for genus abundancies vs. SCFA and alpha diversity all together:

matrix_all_1 <- cor(abundances_gen[, c(4:16)], abundances_gen[,c(17:55)] , method = "spearman")
corrplot(matrix_all_1, method="circle", tl.col = "black", tl.srt = 90, tl.cex =0.9, col.lim = c(-1,1), insig = "blank")

matrix_all_2 <- cor(abundances_gen[, c(4:16)], abundances_gen[,c(56:94)] , method = "spearman")
corrplot(matrix_all_2, method="circle", tl.col = "black", tl.srt = 90, tl.cex =0.9, col.lim = c(-1,1), insig = "blank")

Correlation with p-value

Family level

abund_fam <- abundances_fam[,3:62]
abund_fam <- abund_fam[, -c(4:13)]

testRes_fam = cor.mtest(abund_fam, conf.level = 0.95)
matrix_fam <- cor(abund_fam, method = "spearman")

corrplot(matrix_fam, p.mat = testRes_fam$p, method="circle", tl.col = "black", tl.srt = 90, tl.cex =0.9, col.lim = c(-1,1), insig = "blank")

# table of p-values
P_val_fam <- as.data.frame(testRes_fam$p)
P_val_fam <- P_val_fam[-c(4:50), -c(1:3)]

P_val_fam <- tibble::rownames_to_column(P_val_fam, "SCFA")

# export table
write_xlsx(P_val_fam, here("03_plots_and_tables", "correlation_p_val_family.xlsx"))

Genus level

abund_gen_1 <- abundances_gen[,4:55]
abund_gen_1 <- abund_gen_1[, -c(4:13)]

testRes_gen_1 <- cor.mtest(abund_gen_1, conf.level = 0.95)
matrix_gen_1 <- cor(abund_gen_1, method = "spearman")


corrplot(matrix_gen_1, p.mat = testRes_gen_1$p, method="circle", tl.col = "black", tl.srt = 90, tl.cex =0.9, col.lim = c(-1,1), insig = "blank")

# table of p-values
P_val_gen_1 <- as.data.frame(testRes_gen_1$p)
P_val_gen_1 <- P_val_gen_1[-c(4:42), -c(1:3)]

abund_gen_2 <- abundances_gen[,4:94]
abund_gen_2 <- abund_gen_2[, -c(4:52)]

testRes_gen_2 <- cor.mtest(abund_gen_2, conf.level = 0.95)
matrix_gen_2 <- cor(abund_gen_2, method = "spearman")

#pdf("my_plot.pdf")
corrplot(matrix_gen_2, p.mat = testRes_gen_2$p, method="circle", tl.col = "black", tl.srt = 90, tl.cex =0.9, col.lim = c(-1,1), insig = "blank")

#dev.off()


# table of p-values
P_val_gen_2 <- as.data.frame(testRes_gen_2$p)
P_val_gen_2 <- P_val_gen_2[-c(4:42), -c(1:3)]

# merge tables 
p_val_gen_all <- cbind(P_val_gen_1, P_val_gen_2)
p_val_gen_all <- tibble::rownames_to_column(p_val_gen_all, "SCFA")

# export table
write_xlsx(p_val_gen_all, here("03_plots_and_tables", "correlation_p_val_genus.xlsx"))